Jonathan Weisberg

Locating IBE in the Bayesian framework

Inference to the Best Explanation (IBE) and Bayesianism are our two most prominent theories of scientific inference. Are they compatible? Van Fraassen famously argued that they are not, concluding that IBE must be wrong since Bayesianism is right. Writers since then, from both the Bayesian and explanationist camps, have usually considered van Fraassen’s argument to be misguided, and have plumped for the view that Bayesianism and IBE are actually compatible. I argue that van Fraassen’s argument is actually not so misguided, and that it causes more trouble for compatibilists than is typically thought. Bayesianism in its dominant, subjectivist form, can only be made compatible with IBE if IBE is made subservient to conditionalization in a way that robs IBE of much of its substance and interest. If Bayesianism and IBE are to be fit together, I argue, a strongly objective Bayesianism is the preferred option. I go on to sketch this objectivist, IBE-based Bayesianism, and offer some preliminary suggestions for its development.

Representation Theorems and the Foundations of Decision Theory

Representation theorems are often taken to provide the foundations for decision theory. First, they are taken to characterize degrees of belief and utilities. Second, they are taken to justify two fundamental rules of rationality: that we should have probabilistic degrees of belief and that we should act as expected utility maximizers. We argue that representation theorems cannot serve either of these foundational purposes, and that recent attempts to defend the foundational importance of representation theorems are unsuccessful. As a result, we should reject these claims, and lay the foundations of decision theory on firmer ground.

Updating, Undermining, and Independence

Sometimes appearances provide epistemic support that gets undercut later. In an earlier paper I argued that standard Bayesian update rules are at odds with this phenomenon because they are ‘rigid’. Here I generalize and bolster that argument. I first show that the update rules of Dempster–Shafer theory and ranking theory are rigid too, hence also at odds with the defeasibility of appearances. I then rebut three Bayesian attempts to solve the problem. I conclude that defeasible appearances pose a more difficult and pervasive challenge for formal epistemology than is currently thought. 1 The Challenge for Bayesianism   1.1 Updating and experience   1.2 The problem   1.3 Objections 2 The Challenge for Dempster–Shafer Theory   2.1 Background on Dempster–Shafer theory   2.2 The problem for Dempster–Shafer theory 3 The Challenge for Ranking Theory 4 The Appeal to Metacognition 5 The Appeal to Richer Inputs 6 The Appeal to a Generic Underminer 7 Conclusion 1 The Challenge for Bayesianism   1.1 Updating and experience   1.2 The problem   1.3 Objections   1.1 Updating and experience   1.2 The problem   1.3 Objections 2 The Challenge for Dempster–Shafer Theory   2.1 Background on Dempster–Shafer theory   2.2 The problem for Dempster–Shafer theory   2.1 Background on Dempster–Shafer theory   2.2 The problem for Dempster–Shafer theory 3 The Challenge for Ranking Theory 4 The Appeal to Metacognition 5 The Appeal to Richer Inputs 6 The Appeal to a Generic Underminer 7 Conclusion

Embedding If and Only If

Some left-nested indicative conditionals are hard to interpret while others seem fine. Some proponents of the view that indicative conditionals have No Truth Values (NTV) use their view to explain why some left-nestings are hard to interpret: the embedded conditional does not express the truth conditions needed by the embedding conditional. Left-nestings that seem fine are then explained away as cases of ad hoc, pragmatic interpretation. We challenge this explanation. The standard reasons for NTV about indicative conditionals (triviality results, Gibbardian standoffs, etc.) extend naturally to NTV about biconditionals. So NTVers about conditionals should also be NTVers about biconditionals. But biconditionals embed much more freely than conditionals. If NTV explains why some left-nested conditionals are hard to interpret, why do biconditionals embed successfully in the very contexts where conditionals do not embed?